{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "00e8db9e-dbd4-4906-9194-7bfdf5158cac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Beispiel für die Anwendung des Python-Pakets MetroloPy:\n",
    "#  Berechnung des Luftdrucks in der Höhe h aus dem Luftdruck p0 auf Meereshöhe und der Skalenhöhe H\n",
    "#  unter Einbeziehung der Unsicherheiten nach der Formel\n",
    "#   p(h) = p0 * exp(-(h-h0)/H)\n",
    "#  Weiterhin wird ein Beispiel für die Durchführung einer Monte-Carlo-Simulation gezeigt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7714ac60-8233-4c8a-ad7e-1fb09ea7ef07",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'de_DE'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np # importiere diverse mathematische Funktionen, \n",
    "                   #  z. B. die in Python nicht von vornherein enthaltene Exponentialfunktion np.exp()\n",
    "                   #  Diese spezielle Funktion ist zwar auch als uc.exp() verfügbar, aber im Paket numpy ist noch viel mehr enthalten, es lohnt sich also ggf., dieses zu importieren.\n",
    "import metrolopy as uc # importiere das Packet MetroloPy zum Arbeiten mit Messunsicherheiten\n",
    "import locale # importiere landesspezifische Einstellungen\n",
    "locale.setlocale(locale.LC_ALL, 'de_DE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0e02ecb0-90ca-4366-96ca-910bbc831498",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Normluftdruck p0 = 101 325 Pa"
      ],
      "text/plain": [
       "Normluftdruck p0 = 101 325 Pa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Definiere den Normluftdruck auf Meereshöhe als Konstante ohne Unsicherheit. \n",
    "# Der Name der Größe wird auf \"Normaldruck\" gesetzt.\n",
    "p0 = uc.gummy(101325, 0, unit='Pa', name = 'Normluftdruck p0')\n",
    "p0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a5427ca-84b7-4cb5-9f7c-6f619e9f53dd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Meereshöhe h0 = 0 m"
      ],
      "text/plain": [
       "Meereshöhe h0 = 0 m"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Definiere die Meereshöhe h0=0 als Konstante ohne Unsicherheit\n",
    "h0 = uc.gummy(0, 0, unit = 'm', name = 'Meereshöhe h0')\n",
    "h0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba75f8fe-c455-4a82-acb3-ba1b33c68bf4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Skalenhöhe H = <span>8000(58)&nbsp;m</span>"
      ],
      "text/plain": [
       "Skalenhöhe H = 8000(58) m"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Definiere die Skalenhöhe H der Atmosphäre als Messergebnis des Typs B mit Rechteckverteilung, \n",
    "#  Wert 8000 m und Halbbreite 100 m. \n",
    "# Der Name der Größe wird auf \"Skalenhöhe\" gesetzt.\n",
    "H = uc.gummy(uc.UniformDist(center=8000,half_width=100), unit='m', utype='B', name = 'Skalenhöhe H')\n",
    "H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6784f68-f586-45fa-b55d-a528f2674283",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Höhe h = <span>450.0(10)&nbsp;m</span>"
      ],
      "text/plain": [
       "Höhe h = 450.0(10) m"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Definiere die Höhe h als Messergebnis des Typs B mit Normalverteilung,\n",
    "#  Wert 450 m und Standardunsicherheit 1 m. \n",
    "#  Der Erweiterungsfaktor k wird auf 1 gesetzt (das ist auch der Standardwert). \n",
    "#  Der Name der Größe wird auf \"Höhe\" gesetzt.\n",
    "h = uc.gummy(uc.NormalDist(450, 1), unit = 'm', k = 1, utype='B', name = 'Höhe h')\n",
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "51f5e76a-27db-4ca7-ac42-9b002beee9a2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Druck p = <span>95783(41)&nbsp;Pa</span>"
      ],
      "text/plain": [
       "Druck p = 95783(41) Pa"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Definiere neue Größe Druck p in Abhängigkeit von p0, h, h0 und H.\n",
    "p = p0*uc.exp(-(h-h0)/H)\n",
    "p.name = 'Druck p'\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a23141f-78b5-4bb8-80f1-51fbc98ca0c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       ".budget_table {\n",
       "    display: table;\n",
       "}\n",
       "    \n",
       ".budget_row {\n",
       "    display: table-row;\n",
       "}\n",
       "\n",
       ".budget_header_row {\n",
       "    display: table-row;\n",
       "    font-weight: bold;\n",
       "}\n",
       "\n",
       ".budget_first_col_header_cell\n",
       "{\n",
       "    display: table-cell;\n",
       "    text-align: center;\n",
       "    border-bottom: solid;\n",
       "    border-width: thin;\n",
       "    padding-left: 5px;\n",
       "    padding-right: 5px;\n",
       "}\n",
       "\n",
       ".budget_header_cell {\n",
       "    display: table-cell;\n",
       "    text-align: center;\n",
       "    border-bottom: solid;\n",
       "    border-width: thin;\n",
       "    padding-left: 5px;\n",
       "    padding-right: 5px;\n",
       "    \n",
       "}\n",
       "\n",
       ".budget_first_col_x_cell {\n",
       "    display: table-cell;\n",
       "    text-align: center;\n",
       "    padding-left: 5px;\n",
       "    padding-right: 5px;\n",
       "    min-width: 8ex;\n",
       "}\n",
       "\n",
       ".budget_x_cell {\n",
       "    display: table-cell;\n",
       "    text-align: center;\n",
       "    padding-left: 5px;\n",
       "    padding-right: 5px;\n",
       "    min-width: 8ex;\n",
       "}\n",
       "</style>\n",
       "<div class=\"budget_table\">\n",
       "    <div class=\"budget_header_row\">\n",
       "        <div class = \"budget_header_cell\">Component</div>\n",
       "        <div class = \"budget_header_cell\">Unit</div>\n",
       "        <div class = \"budget_header_cell\">Value</div>\n",
       "        <div class = \"budget_header_cell\"><span><i>u</i></span></div>\n",
       "        <div class = \"budget_header_cell\">Type</div>\n",
       "        <div class = \"budget_header_cell\">|<span>&part;<i>y</i>/&part;<i>x</i></span>|</div>\n",
       "        <div class = \"budget_header_cell\"><span><i>s</i></span></div>\n",
       "    </div>\n",
       "    <div class=\"budget_row\">\n",
       "        <div class=\"budget_first_col_x_cell\">Skalenhöhe H</div>\n",
       "        <div class=\"budget_x_cell\">m</div>\n",
       "        <div class=\"budget_x_cell\">8000</div>\n",
       "        <div class=\"budget_x_cell\">58</div>\n",
       "        <div class=\"budget_x_cell\">B</div>\n",
       "        <div class=\"budget_x_cell\">0.67</div>\n",
       "        <div class=\"budget_x_cell\">0.96</div>\n",
       "    </div>\n",
       "    <div class=\"budget_row\">\n",
       "        <div class=\"budget_first_col_x_cell\">Höhe h</div>\n",
       "        <div class=\"budget_x_cell\">m</div>\n",
       "        <div class=\"budget_x_cell\">450.0</div>\n",
       "        <div class=\"budget_x_cell\">1.0</div>\n",
       "        <div class=\"budget_x_cell\">B</div>\n",
       "        <div class=\"budget_x_cell\">12</div>\n",
       "        <div class=\"budget_x_cell\">0.29</div>\n",
       "    </div>\n",
       "    <div class=\"budget_row\">\n",
       "        <div class=\"budget_first_col_x_cell\">Normluftdruck p0</div>\n",
       "        <div class=\"budget_x_cell\">Pa</div>\n",
       "        <div class=\"budget_x_cell\">101&thinsp;325&nbsp;Pa</div>\n",
       "        <div class=\"budget_x_cell\">0</div>\n",
       "        <div class=\"budget_x_cell\">  </div>\n",
       "        <div class=\"budget_x_cell\">0.0</div>\n",
       "        <div class=\"budget_x_cell\">0</div>\n",
       "    </div>\n",
       "    <div class=\"budget_row\">\n",
       "        <div class=\"budget_first_col_header_cell\">Meereshöhe h0</div>\n",
       "        <div class=\"budget_header_cell\">m</div>\n",
       "        <div class=\"budget_header_cell\">0&nbsp;m</div>\n",
       "        <div class=\"budget_header_cell\">0</div>\n",
       "        <div class=\"budget_header_cell\">  </div>\n",
       "        <div class=\"budget_header_cell\">0.0</div>\n",
       "        <div class=\"budget_header_cell\">0</div>\n",
       "    </div>\n",
       "    <div class=\"budget_row\">\n",
       "        <div class=\"budget_first_col_header_cell\"><span><i>u<sub>c</sub></i> type B</div>\n",
       "        <div class=\"budget_header_cell\">Pa</div>\n",
       "        <div class=\"budget_header_cell\">  </div>\n",
       "        <div class=\"budget_header_cell\">41</div>\n",
       "        <div class=\"budget_header_cell\">B</div>\n",
       "        <div class=\"budget_header_cell\">  </div>\n",
       "        <div class=\"budget_header_cell\">1.00</div>\n",
       "    </div>\n",
       "    <div class=\"budget_row\">\n",
       "        <div class=\"budget_first_col_x_cell\">Druck p</div>\n",
       "        <div class=\"budget_x_cell\">Pa</div>\n",
       "        <div class=\"budget_x_cell\">95783</div>\n",
       "        <div class=\"budget_x_cell\">41</div>\n",
       "        <div class=\"budget_x_cell\">  </div>\n",
       "        <div class=\"budget_x_cell\">  </div>\n",
       "        <div class=\"budget_x_cell\">  </div>\n",
       "    </div>\n",
       "</div>\n",
       "</div>"
      ],
      "text/plain": [
       "       Component Unit      Value   u Type |dy/dx|    s\n",
       "    Skalenhöhe H    m       8000  58    B    0.67 0.96\n",
       "          Höhe h    m      450.0 1.0    B      12 0.29\n",
       "Normluftdruck p0   Pa 101 325 Pa   0          0.0    0\n",
       "   Meereshöhe h0    m        0 m   0          0.0    0\n",
       " Combined type B   Pa             41    B         1.00\n",
       "         Druck p   Pa      95783  41                  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Zeige das Budget für p mit allen Eingangsgrößen p0, h, h0 und H.\n",
    "#  Da p0 und h0 als Konstanten ohne Unsicherheit definiert wurden, \n",
    "#  tragen sie erwartungsgemäß auch nicht zur kombinierten Unsicherheit bei.\n",
    "p.budget([p0, h0, h, H])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63a1176b-c770-4441-8031-7398985e06aa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# erzeuge die Monte-Carlo-simulierten Datensätze für [h, H, p]\n",
    "# Hilfe hierzu z. B. unter https://nrc-cnrc.github.io/MetroloPy/_build/html/hand_made_doc.html#gummy-properties-and-methods-related-to-monte-carlo-simulation\n",
    "#uc.gummy.simulate([h, H, p]) #standardmäßig werden 1e6 zufällige Datensätze pro Größe berechnet\n",
    "uc.gummy.simulate([h, H, p], n=1e7) #mehr simulierte Datensätze (Standard ist 1e6) brauchen zwar mehr Rechenzeit, zeigen dafür aber manche Eigenschaften der Histogramme deutlicher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f827189-ec5a-4e9b-8a60-02ac891c8a3a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# zeichne Histogramme für alle Größen mit Unsicherheit\n",
    "# Hilfe hierzu z. B. unter https://nrc-cnrc.github.io/MetroloPy/_build/html/_static/tutorial.html\n",
    "#h.name='Höhe'\n",
    "#h.p=0.68\n",
    "#h.cimethod='symmetric'\n",
    "h.hist(title='Höhe', xlabel='h  (m)', ci_marker=False)\n",
    "#H.hist(xlabel='H  (m)',ci_marker=False)\n",
    "#H.name='Skalenhöhe'\n",
    "#H.p=0.68\n",
    "#H.cimethod='symmetric'\n",
    "H.hist(title='Skalenhöhe', xlabel='H  (m)',ci_marker=False)\n",
    "#p.name='Druck'\n",
    "#p.p=0.68\n",
    "#p.cimethod='symmetric'\n",
    "#p.hist(title='Druck', xlabel='p  (Pa)',ci_marker=False) #Eine Anzahl von 50 bins ist Standard.\n",
    "p.hist(title='Druck', xlabel='p  (Pa)',ci_marker=False, bins=1000) #Eine erhöhte Zahl von bins ist nur sinnvoll, wenn die Zahl der generierten Datensätze ebenfalls erhöht ist."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}